Combining Model Inference and Passive Testing in the Same Framework to Test Industrial Systems

Abstract

Many software engineering approaches often rely on formal models to automate some steps of the software life cycle, particularly the testing phase. Even though automation sounds attractive, writing models is usually a tedious and error-prone task. In addition, with industrial software systems, models are often not up-to-date. Hence, testing these systems becomes problematic. In this context, this article proposes a framework called Autofunk to test production systems by combining two approaches: model generation and passive testing. Given a large set of events collected from a production system, Autofunk combines an expert system, formal models and machine learning to infer symbolic models while preventing over-generalisation. Afterwards, these models are considered to passively test whether another system is conforming to the models. As the generated models do not express all the possible behaviours that should happen, we define conformance with four specialised implementation relations.